Thesis Topology Optimization Crash Structures, Volkswagen AG

Thesis Topology Optimization Crash Structures

Volkswagen AG

Location:
Germany, Wolfsburg

Category:
Research and Development

Contract Type:
Not provided

Salary:

Not provided

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Job Description:

We, the pre-development body system of the Volkswagen brand, work on interdisciplinary pre-development projects and manage strategic innovation topics in body development. Our interdisciplinary team of experts develops the highlights of future vehicle generations in all areas of vehicle construction. We use the latest scientific methods and findings and bring them to series production. In the development of future body concepts, occupant protection and lightweight construction are decisive design criteria. Methods for structural optimisation can play a decisive role here. However, due to the special requirements of crash simulation, conventional methods can only be used to a limited extent, which makes it necessary to develop new approaches.

Job Responsibility:

Participation in the development of machine learning-based optimisation software for crash structures
setup, execution and evaluation of FEM simulations
validation of developed methods in software as well as in simulation
research and study algorithms for geometry generation
working on the topics within the framework of an agile way of working

Requirements:

Students in mechanical engineering, aerospace engineering, simulation technology, computer science or with comparable qualifications
very good academic performance and a completed bachelor's degree
basic knowledge of finite element analysis (preferably explicit FEA)
good knowledge of object-oriented programming (preferably Python)
German and English Language Level B2
analytical skills, initiative, high commitment, teamwork and organizational skills

What we offer:

Opportunity to participate in an interdisciplinary team
flexible remote working up to 40%

Additional Information:

Job Posted:
October 15, 2025

Employment Type:

Fulltime

Work Type:

Hybrid work

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